Work and Commuting in Census Metropolitan Areas, 1996 to 2001
Bibliographic record
Abstract
The report examined the location of jobs in 27 census metropolitan areas, paying particular attention to developments in Quebec, Montreal, Ottawa-Hull, Toronto, Winnipeg, Calgary, Edmonton and Vancouver. It also analysed the modes commuters used to travel to work, emphasising public transit and car (as driver or passenger) commute modes. While Canadian metropolitan areas continue to be characterized by a strong concentration of jobs in the downtown core, employment grew faster in the suburbs of Canada's largest metropolitan areas than in the city centres between 1996 and 2001. One characteristic of increasing employment in suburban locations is the shifting of manufacturing activities from the core of the city to the suburbs. Retail trade also shifted away from the central core towards more suburban locations. Relatively few workers employed outside the city centre commuted on public transit, rather, most drove or were a passenger in a car. This tendency to commute by car increased the farther the job was located from the city centre. Furthermore commute patterns have become more complex, with growth in suburb-to-suburb commutes outpacing traditional commute paths within the city centre, and between the city centre and suburbs. Commuters travelling from suburb to suburb were also much more likely to drive than take public transit. Despite the decentralization of jobs occurring in the metropolitan areas, public transit did not lose its share of commuters between 1996 and 2001. While more car traffic headed to jobs in the suburbs, a larger share of commuters heading for the city centre took public transit. This kept the total share of commuters who took public transit stable between 1996 and 2001. The report also found that jobs in the downtown core were higher skilled and higher paid, and that earnings increased faster for jobs in the city centre between 1996 and 2001. The report uses the 1996 and 2001 censuses of Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".